Bump mcp and other dependencies for Dependabot alerts

docs/mcp/server.md

@@ -127,6 +127,7 @@ async def sampling_callback(
                 annotations=None,
                 meta=None,
             ),
+            meta=None,
         )
     ]
     """

examples/pydantic_ai_examples/weather_agent_gradio.py

@@ -99,7 +99,6 @@ def select_data(message: gr.SelectData) -> str:
     past_messages = gr.State([])
     chatbot = gr.Chatbot(
         label='Packing Assistant',
-        type='messages',
         avatar_images=(None, 'https://ai.pydantic.dev/img/logo-white.svg'),
         examples=[
             {'text': 'What is the weather like in Miami?'},

examples/pyproject.toml

@@ -56,8 +56,8 @@ dependencies = [
     "rich>=13.9.2",
     "uvicorn>=0.32.0",
     "devtools>=0.12.2",
-    "gradio>=5.9.0",
-    "mcp[cli]>=1.4.1",
+    "gradio>=5.31.0",
+    "mcp[cli]>=1.25.0",
     "modal>=1.0.4",
     "duckdb>=1.3.2",
     "datasets>=4.0.0",

pydantic_ai_slim/pydantic_ai/_mcp.py

@@ -33,11 +33,12 @@ def map_from_mcp_params(params: mcp_types.CreateMessageRequestParams) -> list[me
             # TODO(Marcelo): We can reuse the `_map_tool_result_part` from the mcp module here.
             if isinstance(content, mcp_types.TextContent):
                 user_part_content: str | Sequence[messages.UserContent] = content.text
-            else:
-                # image content
+            elif isinstance(content, mcp_types.ImageContent):
                 user_part_content = [
                     messages.BinaryContent(data=base64.b64decode(content.data), media_type=content.mimeType)
                 ]
+            else:
+                raise NotImplementedError(f'Unsupported user content type: {type(content).__name__}')
 
             request_parts.append(messages.UserPromptPart(content=user_part_content))
         else:
@@ -47,7 +48,10 @@ def map_from_mcp_params(params: mcp_types.CreateMessageRequestParams) -> list[me
                 pai_messages.append(messages.ModelRequest(parts=request_parts))
                 request_parts = []
 
-            response_parts.append(map_from_sampling_content(content))
+            if isinstance(content, (mcp_types.TextContent, mcp_types.ImageContent, mcp_types.AudioContent)):
+                response_parts.append(map_from_sampling_content(content))
+            else:
+                raise NotImplementedError(f'Unsupported assistant content type: {type(content).__name__}')
 
     if response_parts:
         pai_messages.append(messages.ModelResponse(parts=response_parts))

pydantic_ai_slim/pydantic_ai/mcp.py

@@ -32,7 +32,7 @@
     from mcp.client.session import ClientSession, ElicitationFnT, LoggingFnT
     from mcp.client.sse import sse_client
     from mcp.client.stdio import StdioServerParameters, stdio_client
-    from mcp.client.streamable_http import GetSessionIdCallback, streamablehttp_client
+    from mcp.client.streamable_http import GetSessionIdCallback, streamable_http_client
     from mcp.shared import exceptions as mcp_exceptions
     from mcp.shared.context import RequestContext
     from mcp.shared.message import SessionMessage
@@ -1134,8 +1134,10 @@ def _transport_client(
         ],
     ]: ...
 
+    # Only used by MCPServerSSE which has a hang bug (https://github.com/modelcontextprotocol/python-sdk/issues/1811).
+    # Remove pragma once https://github.com/modelcontextprotocol/python-sdk/pull/1838 is released.
     @asynccontextmanager
-    async def client_streams(
+    async def client_streams(  # pragma: no cover
         self,
     ) -> AsyncIterator[
         tuple[
@@ -1144,7 +1146,7 @@ async def client_streams(
         ]
     ]:
         if self.http_client and self.headers:
-            raise ValueError('`http_client` is mutually exclusive with `headers`.')  # pragma: no cover
+            raise ValueError('`http_client` is mutually exclusive with `headers`.')
 
         transport_client_partial = functools.partial(
             self._transport_client,
@@ -1154,7 +1156,8 @@ async def client_streams(
         )
 
         if self.http_client is not None:
-            # TODO: Clean up once https://github.com/modelcontextprotocol/python-sdk/pull/1177 lands.
+            # sse_client still uses the old httpx_client_factory API (streamable_http_client uses http_client).
+            # This wrapper is needed until sse_client is updated to match streamable_http_client's API.
             @asynccontextmanager
             async def httpx_client_factory(
                 headers: dict[str, str] | None = None,
@@ -1218,8 +1221,8 @@ def __get_pydantic_core_schema__(cls, _: Any, __: Any) -> CoreSchema:
         )
 
     @property
-    def _transport_client(self):
-        return sse_client  # pragma: no cover
+    def _transport_client(self):  # pragma: no cover
+        return sse_client
 
     def __eq__(self, value: object, /) -> bool:
         return super().__eq__(value) and isinstance(value, MCPServerSSE) and self.url == value.url
@@ -1282,8 +1285,36 @@ def __get_pydantic_core_schema__(cls, _: Any, __: Any) -> CoreSchema:
         )
 
     @property
-    def _transport_client(self):
-        return streamablehttp_client
+    def _transport_client(self):  # pragma: no cover
+        return streamable_http_client
+
+    @asynccontextmanager
+    async def client_streams(
+        self,
+    ) -> AsyncIterator[
+        tuple[
+            MemoryObjectReceiveStream[SessionMessage | Exception],
+            MemoryObjectSendStream[SessionMessage],
+        ]
+    ]:
+        # The new streamable_http_client API only accepts url and http_client.
+        # We need to create an httpx.AsyncClient with the desired timeout and headers.
+        if self.http_client is not None:
+            async with streamable_http_client(self.url, http_client=self.http_client) as (
+                read_stream,
+                write_stream,
+                *_,
+            ):
+                yield read_stream, write_stream
+        else:
+            timeout = httpx.Timeout(self.timeout, read=self.read_timeout)
+            async with httpx.AsyncClient(timeout=timeout, headers=self.headers) as client:
+                async with streamable_http_client(self.url, http_client=client) as (
+                    read_stream,
+                    write_stream,
+                    *_,
+                ):
+                    yield read_stream, write_stream
 
     def __eq__(self, value: object, /) -> bool:
         return super().__eq__(value) and isinstance(value, MCPServerStreamableHTTP) and self.url == value.url

pydantic_ai_slim/pyproject.toml

@@ -104,9 +104,9 @@ cli = [
     "pyperclip>=1.9.0",
 ]
 # MCP
-mcp = ["mcp>=1.18.0"]
+mcp = ["mcp>=1.25.0"]
 # FastMCP
-fastmcp = ["fastmcp>=2.12.0"]
+fastmcp = ["fastmcp>=2.14.0"]
 # Evals
 evals = ["pydantic-evals=={{ version }}"]
 # UI

tests/test_mcp.py

@@ -1678,7 +1678,11 @@ def test_map_from_pai_messages_with_binary_content():
     assert system_prompt == ''
     assert [m.model_dump(by_alias=True) for m in sampling_msgs] == snapshot(
         [
-            {'role': 'user', 'content': {'type': 'text', 'text': 'text message', 'annotations': None, '_meta': None}},
+            {
+                'role': 'user',
+                'content': {'type': 'text', 'text': 'text message', 'annotations': None, '_meta': None},
+                '_meta': None,
+            },
             {
                 'role': 'user',
                 'content': {
@@ -1688,6 +1692,7 @@ def test_map_from_pai_messages_with_binary_content():
                     'annotations': None,
                     '_meta': None,
                 },
+                '_meta': None,
             },
         ]
     )